| Deep learning identifies accurate burst locations in water distribution networks X Zhou, Z Tang, W Xu, F Meng, X Chu, K Xin, G Fu Water research 166, 115058, 2019 | 176 | 2019 |
| A distributed synchronous SGD algorithm with global Top-k sparsification for low bandwidth networks S Shi, Q Wang, K Zhao, Z Tang, Y Wang, X Huang, X Chu 2019 IEEE 39th International Conference on Distributed Computing Systems …, 2019 | 144 | 2019 |
| Communication-efficient distributed deep learning: A comprehensive survey Z Tang, S Shi, X Chu, W Wang, B Li arXiv preprint arXiv:2003.06307, 2020 | 120 | 2020 |
| A Convergence Analysis of Distributed SGD with Communication-Efficient Gradient Sparsification. S Shi, K Zhao, Q Wang, Z Tang, X Chu IJCAI, 3411-3417, 2019 | 80 | 2019 |
| The impact of GPU DVFS on the energy and performance of deep learning: An empirical study Z Tang, Y Wang, Q Wang, X Chu Proceedings of the Tenth ACM International Conference on Future Energy …, 2019 | 74 | 2019 |
| FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks C He, AD Shah, Z Tang, DFAN Sivashunmugam, K Bhogaraju, M Shimpi, ... International Workshop on Trustable, Verifiable and Auditable Federated …, 2021 | 63 | 2021 |
| Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning Z Tang, Y Zhang, S Shi, X He, B Han, X Chu ICML 2022, 2022 | 60 | 2022 |
| Benchmarking the Performance and Energy Efficiency of AI Accelerators for AI Training Y Wang, Q Wang, S Shi, X He, Z Tang, K Zhao, X Chu 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet …, 2020 | 56 | 2020 |
| Communication-efficient decentralized learning with sparsification and adaptive peer selection Z Tang, S Shi, X Chu 2020 IEEE 40th International Conference on Distributed Computing Systems …, 2020 | 55 | 2020 |
| GossipFL: A Decentralized Federated Learning Framework With Sparsified and Adaptive Communication Z Tang, S Shi, B Li, X Chu IEEE Transactions on Parallel and Distributed Systems 34 (3), 909-922, 2022 | 47 | 2022 |
| A quantitative survey of communication optimizations in distributed deep learning S Shi, Z Tang, X Chu, C Liu, W Wang, B Li IEEE Network 35 (3), 230-237, 2020 | 38 | 2020 |
| Deep learning identifies leak in water pipeline system using transient frequency response Z Liao, H Yan, Z Tang, X Chu, T Tao Process Safety and Environmental Protection 155, 355-365, 2021 | 33 | 2021 |
| Layer-wise adaptive gradient sparsification for distributed deep learning with convergence guarantees S Shi, Z Tang, Q Wang, K Zhao, X Chu 24th European Conference on Artificial Intelligence, ECAI 2020, 2019 | 24 | 2019 |
| Benchmarking the performance and power of AI accelerators for AI training Y Wang, Q Wang, S Shi, X He, Z Tang, K Zhao, X Chu arXiv preprint arXiv:1909.06842, 2019 | 14 | 2019 |
| Data Resampling for Federated Learning with Non-IID Labels Z Tang, Z Hu, S Shi, Y Cheung, Y Jin, Z Ren, X Chu FTL-IJCAI 2021, 2021 | 12 | 2021 |
| Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models X He, S Wang, S Shi, Z Tang, Y Wang, Z Zhao, J Dai, R Ni, X Zhang, X Liu, ... 2019 IEEE International Conference on Big Data (Big Data), 4839-4844, 2019 | 12 | 2019 |
| FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs Z Tang, Y Wang, X He, L Zhang, X Pan, Q Wang, R Zeng, K Zhao, S Shi, ... The 32nd International Joint Conference on Artificial Intelligence …, 2023 | 8 | 2023 |
| NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension X He, J Yao, Y Wang, Z Tang, KC Cheung, S See, B Han, X Chu AAAI 2023, 2022 | 6 | 2022 |
| FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training Z Tang, X Chu, RY Ran, S Lee, S Shi, Y Zhang, Y Wang, AQ Liang, ... arXiv preprint arXiv:2303.01778, 2023 | 5 | 2023 |
| VMRNN: Integrating Vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecasting Y Tang, P Dong, Z Tang, X Chu, J Liang CVPR Workshop 2024, 2024 | 4 | 2024 |
Xiaowen ChuProfessor, Data Science and Analytics, HKUST(GZ)在 ust.hk 的电子邮件经过验证
